In this paper, dynamic predictive monitoring schemes based on dynamic latent variable models are proposed. We consider the most typical case in industrial data where dynamics usually exist in a reduced dimensional subspace. First, using dynamic latent variable models, predictions are made to reduce the dynamic latent space variability and focus on the unpredictable variabilities for process monitoring, leading to reduced control regions without reducing confidence levels. A general expression is developed to decompose the overall uncertainty into dynamic prediction errors and static variabilities. Second, monitoring based on multistep ahead prediction windows are used to generate fault-free predictions whenever a fault is detected. Third, we illustrate that oblique projections in dynamic latent variable models are required to separate static variabilities from dynamic latent variables. A detected fault is further classified as a static fault or a dynamic fault based on different monitoring indices. Case studies on a simulation data set and the Tennessee Eastman data set the effectiveness of the predictive demonstrate monitoring schemes.